Hierarchical Style Modeling: A generative framework for Style-Centric Generation of 3D Models

This work focuses on incorporating style - specifically visual style - into procedural content generation processes. Specifically, I model style as a series of constraints that must be satisfied while an object is generated

Environmental impact analysis of alternative pallet management systems

Pallets, the most common unit-load platform, allow the transportation of goods in an efficient and reliable way. Every year, 700 million new pallets are manufactured and become part of the approximately 2 billion pallets that are in circulation in the U.S. The total life-cycle environmental impact of pallets depends on materials, manufacturing, handling processes, and the disposal practice (end-of-life). Plastic pallets can be lighter and might last longer but their manufacturing processes are energy intensive and could contribute significantly to greenhouse gas (GHG) emissions. On the other hand, wooden pallets can be cheaper and easily repaired but present a shorter life. The ability to control the end-of-life of the pallets and the associated environmental impacts of each scenario allows pallet pooling service companies to provide logistics arrangements that are attractive to those companies seeking to better manage their carbon footprint. The appropriate choice of pallet type (i.e. material, durability, etc.) and management structure (e.g. cost, lease vs. buy, etc.) may lead to a more sustainable logistics operation. The purpose of this study is to provide a model that would determine the impact of pallet materials, manufacturing, distribution, and take back operations on an environmental performance metric (such as carbon dioxide emissions) as well as cost. Mixed integer programming (a minimum cost multi-commodity network flow problem) is used to design the system that determines the mix of pallets (type, quantity, and pallet management system) for product distribution that balances overall environmental impacts and costs according to companies\u27 needs. Such a tool would aid in decision making at the logistics and distribution levels. Results from a case study of a large grocery distributor/retailer in the Northeast is presented

X-Risk Analysis for AI Research

Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities. Current AI research lacks a systematic discussion of how to manage long-tail risks from AI systems, including speculative long-term risks. Keeping in mind the potential benefits of AI, there is some concern that building ever more intelligent and powerful AI systems could eventually result in systems that are more powerful than us; some say this is like playing with fire and speculate that this could create existential risks (x-risks). To add precision and ground these discussions, we provide a guide for how to analyze AI x-risk, which consists of three parts: First, we review how systems can be made safer today, drawing on time-tested concepts from hazard analysis and systems safety that have been designed to steer large processes in safer directions. Next, we discuss strategies for having long-term impacts on the safety of future systems. Finally, we discuss a crucial concept in making AI systems safer by improving the balance between safety and general capabilities. We hope this document and the presented concepts and tools serve as a useful guide for understanding how to analyze AI x-risk

An Overview of Catastrophic AI Risks

Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts, policymakers, and world leaders regarding the potential for increasingly advanced AI systems to pose catastrophic risks. Although numerous risks have been detailed separately, there is a pressing need for a systematic discussion and illustration of the potential dangers to better inform efforts to mitigate them. This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories: malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs; organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents; and rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans. For each category of risk, we describe specific hazards, present illustrative stories, envision ideal scenarios, and propose practical suggestions for mitigating these dangers. Our goal is to foster a comprehensive understanding of these risks and inspire collective and proactive efforts to ensure that AIs are developed and deployed in a safe manner. Ultimately, we hope this will allow us to realize the benefits of this powerful technology while minimizing the potential for catastrophic outcomes

Design and Analysis of Mechanical Gripper Technologies for Handling Mesh Electrodes in Electrolysis Cell Production

As climate change accelerates, the demand for green energy is growing significantly. Due to the intermittent nature of renewable energy, the need for long-term storage is growing at the same rate. Hydrogen presents itself as a promising option for long-term storage, the need for electrolysis plants is therefore increasing significantly. Solutions for scaling up alkaline electrolysis production are currently lacking, particularly in the handling of large mesh electrodes. Therefore, new gripping concepts and technologies have to be developed to enable precise and automated handling of the electrodes, as established handling methods have failed due to the porous, limp and weakly magnetic material properties. This paper therefore presents two new ingressive gripping technologies in the form of individual gripping elements, which can later be combined to form a gripper. The technologies identified here are based on a threaded structure on the one hand and a spiral-like structure on the other. Depending on the mesh geometry to be handled, the gripper elements are designed accordingly. In order to grip the mesh, the gripping element is moved forward and turned at the same time. For verification, sample gripper elements were tested for a range of mesh geometries. The individual gripper elements were produced using selective Laser melting process (SLM), as the fine structures would be exceedingly challenging as well as very costly to produce using conventional manufacturing methods. The gripper elements were tested for three aspects of the handling process: Reliability, retention force and precision. The results in finer meshes show a high holding force for the spiral structures, while the screw structures show more potential in precision. In terms of performance in finer meshes, both structures have potential for use in mesh electrodes, with the low retention force of the screw structures due to the increasing imprecision of the SLM process

General and Specific Displacement Effects of Police Crackdowns: Criminal Events and "Local" Criminals

Geographically focused police crackdowns have widely diffused amongst larger American police departments in the past decade and have been recently cited in a Police Executive Research Forum survey as the most commonly used tactic to combat violent crime. Evidence from a number of randomized control trials, systematic reviews, and meta-analyses suggests that these interventions have the ability to reduce crime without displacing it to nearby locations. However, virtually every study of crime displacement in response to a geographically concentrated police intervention focuses on small buffer zones immediately surrounding the intervention location. While crime may not displace just around the corner, to date, few studies have tested displacement beyond this limited geographic constraint. During the summer of 2011 the Metropolitan Police Department of Washington D.C. implemented a geographically focused arrest-driven police crackdown called the Summer Crime Initiative (SCI). The current work aims to examine the impact of the SCI on the volume and placement of robbery through a quasi-experimental research design. By developing a theoretically informed framework, a broader set of hypotheses regarding local and non-local crime displacement are tested. The results of this study confirm prior research on crime displacement. Despite reductions in robbery, there is no evidence that these offenses or offenders were displaced within or beyond two blocks of the intervention sites

Toward managing catastrophic AI risks

Artificial intelligence (AI) has rapidly improved over the past decade, leading to widespread adoption of AI systems and demonstrating the potential for AI to greatly benefit society. However, as with any powerful new technology, AI introduces risks that must be managed to fully realize these benefits. Recent breakthroughs in the generality of AI systems have drawn increased attention to AI risks, including those of a potentially catastrophic nature. To help manage these anticipated risks, we take a defense in depth approach, combining different areas of AI safety research to address different aspects of AI risk. We present research on making AI systems more robust to adversarial influence, monitoring AIs for hidden behavior and trojans, enabling AIs to understand and adhere to human values, and finally addressing systemic problems to enable increased transparency.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo termsThe student, Mantas Mazeika, accepted the attached license on 2024-04-23 at 09:49.The student, Mantas Mazeika, submitted this Dissertation for approval on 2024-04-23 at 09:57.This Dissertation was approved for publication on 2024-04-24 at 15:16.DSpace SAF Submission Ingestion Package generated from Vireo submission #20566 on 2024-09-16 at 00:35:5